An effective Honey Badger Algorithm based Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution Systems

Madhubabu Thiruveedula; K Asokan; J B V Subrahmanyam

doi:10.17485/IJST/v17i13.3283

Article

An effective Honey Badger Algorithm based Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution Systems

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v17i13.3283

Year: 2024, Volume: 17, Issue: 13, Pages: 1357-1367

Original Article

An effective Honey Badger Algorithm based Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations in Radial Distribution Systems

Madhubabu Thiruveedula^1*, K Asokan², J B V Subrahmanyam³

¹Research Scholar, Department of Electrical Engineering, Annamalai University, Annamalai Nagar, Tamil Nadu, India
²Associate Professor, Department of Electrical and Electronics Engineering, Government College of Engineering, Bargur, Tamil Nadu, India
³Professor and Principal, Department of Electrical and Electronics Engineering, Holy Mary Institute of Technology and Science, Hyderabad, Telangana, India

*Corresponding Author
Email: [email protected]

Received Date:01 January 2024, Accepted Date:06 March 2024, Published Date:28 March 2024

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To solve the multi-objective optimization problem in Radial Distribution Systems (RDS) using intelligent computational algorithm. The proposed work considers the recently developed Electric Vehicle Charging Stations (EVCSs) to minimize the network loss, reduce the Average Voltage Deviation Index (AVDI) and improve the Voltage Stability Index (VSI) of RDS. Methods: A new and novel optimization method of Honey Badger Algorithm (HBA) is proposed to solve the multi objective optimization problem. HBA is divided into two phases such as digging phase and honey phase, which are efficiently determining the optimal location and required value of EVCSs. The MATLAB 14.0 software is sued to implement the HBA methodology. The control parameters HBA such as population size is 40 and number of iteration is 200 iterations Findings: The power loss minimization of proposed test system is 48.82% improved when compared with base case method and 2.5 % improved than the other existing methods viz. Particle Swam Optimization (PSO), Flower Pollination Algorithm (FPA), Cuckoo Search Algorithm (CSA) and Teaching Learning Based (TLBO). Similarly, the Average Voltage Deviation Index is 43.42% improved when compared with base case method and 1.2 % improved than the other existing methods. Novelty: The proposed HBA effectively improves performance of RDS under increased loading conditions by tuning of the best location and optimal size of the EVCSs.

Keywords: Radial distribution system, Electric Vehicles, Charging Stations, Voltage stability, Power loss and Honey Badger algorithm

References

Yuvaraj T, Devabalaji KR, Srinivasan S, Prabaharan N, Hariharan R, Alhelou HH, et al. Comparative analysis of various compensating devices in energy trading radial distribution system for voltage regulation and loss mitigation using Blockchain technology and Bat Algorithm. Energy Reports. 2021;7:8312–8321. Available from: https://doi.org/10.1016/j.egyr.2021.08.184
Huiling T, Jiekang W, Fan W, Lingmin C, Zhijun L, Haoran Y. An Optimization Framework for Collaborative Control of Power Loss and Voltage in Distribution Systems With DGs and EVs Using Stochastic Fuzzy Chance Constrained Programming. IEEE Access. 2020;8:49013–49027. Available from: https://doi.org/10.1109/ACCESS.2020.2976510
Ahmad F, Khalid M, Panigrahi BK. An enhanced approach to optimally place the solar powered electric vehicle charging station in distribution network. Journal of Energy Storage. 2021;42:103090. Available from: https://doi.org/10.1016/j.est.2021.103090
Bilal M, Rizwan M. Integration of electric vehicle charging stations and capacitors in distribution systems with vehicle-to-grid facility. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects . 2021. Available from: https://doi.org/10.1080/15567036.2021.1923870
Rajesh P, Shajin FH. Optimal allocation of EV charging spots and capacitors in distribution network improving voltage and power loss by Quantum-Behaved and Gaussian Mutational Dragonfly Algorithm (QGDA) Electric Power Systems Research. 2021;194:107049. Available from: https://doi.org/10.1016/j.epsr.2021.107049
Ponnam VKB, Swarnasri K. Multi-Objective Optimal Allocation of Electric Vehicle Charging Stations and Distributed Generators in Radial Distribution Systems using Metaheuristic Optimization Algorithms. Engineering, Technology & Applied Science Research. 2020;10(3):5837–5844. Available from: https://doi.org/10.48084/etasr.3517
Shahbazi A, Cheshmehbeigi HM, Abdi H, Shahbazitabar M. Probabilistic Optimal Allocation of Electric Vehicle Charging Stations Considering the Uncertain Loads by Using the Monte Carlo Simulation Method. Journal of Operation and Automation in Power Engineering. 2023;11(4):277–284. Available from: https://joape.uma.ac.ir/article_1925_354124a18f01cf294e437570d4e6df46.pdf
Gong D, Tang M, Buchmeister B, Zhang H. Solving Location Problem for Electric Vehicle Charging Stations—A Sharing Charging Model. IEEE Access. 2019;7:138391–138402. Available from: https://doi.org/10.1109/ACCESS.2019.2943079
Ji D, Lv M, Yang J, Yi W. Optimizing the Locations and Sizes of Solar Assisted Electric Vehicle Charging Stations in an Urban Area. IEEE Access. 2020;8:112772–112782. Available from: https://doi.org/10.1109/ACCESS.2020.3003071
Deb S, Tammi K, Gao XZZ, Kalita K, Mahanta P. A Hybrid Multi-Objective Chicken Swarm Optimization and Teaching Learning Based Algorithm for Charging Station Placement Problem. IEEE Access. 2020;8:92573–92590. Available from: https://doi.org/10.1109/ACCESS.2020.2994298
Bilal M, Rizwan M, Alsaidan I, Almasoudi FM. AI-Based Approach for Optimal Placement of EVCS and DG With Reliability Analysis. IEEE Access. 2021;9:154204–154224. Available from: https://doi.org/10.1109/ACCESS.2021.3125135
Liu L, Xie F, Huang Z, Wang M. Multi-Objective Coordinated Optimal Allocation of DG and EVCSs Based on the V2G Mode. Processes. 2021;9(1):1–18. Available from: https://doi.org/10.3390/pr9010018
Fokui WST, Saulo MJ, Ngoo L. Optimal placement of electric vehicle charging stations in a distribution network with randomly distributed rooftop photovoltaic systems. Ieee Access. 2021;9:132397–132411. Available from: https://doi.org/10.1109/ACCESS.2021.3112847
Li J, Liu Z, Wang X. Public charging station location determination for electric ride-hailing vehicles based on an improved genetic algorithm. Sustainable Cities and Society. 2021;74:103181. Available from: https://doi.org/10.1016/j.scs.2021.103181
Reddy MSK, Selvajyothi K. Optimal placement of electric vehicle charging station for unbalanced radial distribution systems. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects. 2020. Available from: https://doi.org/10.1080/15567036.2020.1731017
Rene EA, Fokui WST, Kouonchie PKN. Optimal allocation of plug-in electric vehicle charging stations in the distribution network with distributed generation. Green Energy and Intelligent Transportation. 2023;2(3):1–12. Available from: https://doi.org/10.1016/j.geits.2023.100094
Yenchamchalit K, Kongjeen Y, Prabpal P, Bhumkittipich K. Optimal Placement of Distributed Photovoltaic Systems and Electric Vehicle Charging Stations Using Metaheuristic Optimization Techniques. Symmetry. 2021;13(12):1–15. Available from: https://doi.org/10.3390/sym13122378
Raval S, Natarajan T, Deb S. A Novel Levy-Enhanced Opposition-Based Gradient-Based Optimizer (LE-OB-GBO) for Charging Station Placement. Electronics. 2023;12(7):1–16. Available from: https://doi.org/10.3390/electronics12071522
Bitencourt L, Abud TP, Dias BH, Borba BSMC, Maciel RS, Quirós-Tortós J. Optimal location of EV charging stations in a neighborhood considering a multi-objective approach. Electric Power Systems Research. 2021;199:107391. Available from: https://doi.org/10.1016/j.epsr.2021.107391
Zhang Y, Wang Y, Li F, Wu B, Chiang YYY, Zhang X. Efficient Deployment of Electric Vehicle Charging Infrastructure: Simultaneous Optimization of Charging Station Placement and Charging Pile Assignment. IEEE Transactions on Intelligent Transportation Systems. 2021;22(10):6654–6659. Available from: https://doi.org/10.1109/TITS.2020.2990694
Zeb MZ, Imran K, Khattak A, Janjua AK, Pal A, Nadeem M, et al. Optimal Placement of Electric Vehicle Charging Stations in the Active Distribution Network. IEEE Access. 2020;8:68124–68134. Available from: https://doi.org/10.1109/ACCESS.2020.2984127
Yuvaraj T, Devabalaji KR, Thanikanti SB, Pamshetti VB, Nwulu NI. Integration of Electric Vehicle Charging Stations and DSTATCOM in Practical Indian Distribution Systems Using Bald Eagle Search Algorithm. IEEE Access. 2023;11:55149–55168. Available from: https://doi.org/10.1109/ACCESS.2023.3280607
Ferraz RSF, Ferraz RSF, Medina ACR, Fardin JF. Multi-objective approach for optimized planning of electric vehicle charging stations and distributed energy resources. Electrical Engineering. 2023;105(6):4105–4117. Available from: https://doi.org/10.1007/s00202-023-01942-z
Lazari V, Chassiakos A. Multi-Objective Optimization of Electric Vehicle Charging Station Deployment Using Genetic Algorithms. Applied Sciences. 2023;13(8):1–19. Available from: https://doi.org/10.3390/app13084867
Hashim FA, Houssein EH, Hussain K, Mabrouk MS, Al-Atabany W. Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems. Mathematics and Computers in Simulation. 2022;192:84–110. Available from: https://doi.org/10.1016/j.matcom.2021.08.013
Elseify MA, Kamel S, Abdel-Mawgoud H, Elattar EE. A Novel Approach Based on Honey Badger Algorithm for Optimal Allocation of Multiple DG and Capacitor in Radial Distribution Networks Considering Power Loss Sensitivity. Mathematics. 2022;10(12):1–26. Available from: https://doi.org/10.3390/math10122081
Hu G, Zhong J, Wei G. SaCHBA_PDN: Modified honey badger algorithm with multi-strategy for UAV path planning. Expert Systems with Applications. 2023;223:119941. Available from: https://doi.org/10.1016/j.eswa.2023.119941
Yang XS. Flower Pollination Algorithm for Global Optimization. In: International Conference on Unconventional Computing and Natural Computation, UCNC 2012, Lecture Notes in Computer Science . (Vol. 7445, pp. 240-249) Berlin, Heidelberg. Springer. 2012.
Yang XS, Deb S. Engineering optimisation by cuckoo search. International Journal of Mathematical Modelling and Numerical Optimisation. 2010;1(4):330–343. Available from: https://www.inderscience.com/offers.php?id=35430
Ponnam VKB, Swarnasri K. Multi-objective optimal allocation of electric vehicle charging stations in radial distribution system using teaching learning based optimization. International Journal of Renewable Energy Research. 2020;10(1):366–377. Available from: https://doi.org/10.20508/ijrer.v10i1.10453.g7882
Zhang H, Ishikawa M. Characterization of particle swarm optimization with diversive curiosity. Neural Computing and Applications. 2009;18(5):409–415. Available from: https://doi.org/10.1007/s00521-009-0252-4
Mirjalili S. The Ant Lion Optimizer. Advances in engineering software. 2015;83:80–98. Available from: https://doi.org/10.1016/j.advengsoft.2015.01.010

Copyright

© 2024 Thiruveedula et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)